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Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

Koen W. De Bock, Kristof Coussement, Arno De Caigny, Roman Słowiński, Bart Baesens, Robert N. Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke and Richard Weber

European Journal of Operational Research, 2024, vol. 317, issue 2, 249-272

Abstract: The ability to understand and explain the outcomes of data analysis methods, with regard to aiding decision-making, has become a critical requirement for many applications. For example, in operational research domains, data analytics have long been promoted as a way to enhance decision-making. This study proposes a comprehensive, normative framework to define explainable artificial intelligence (XAI) for operational research (XAIOR) as a reconciliation of three subdimensions that constitute its requirements: performance, attributable, and responsible analytics. In turn, this article offers in-depth overviews of how XAIOR can be deployed through various methods with respect to distinct domains and applications. Finally, an agenda for future XAIOR research is defined.

Keywords: Decision analysis; XAI; Explainable artificial intelligence; Interpretable machine learning; XAIOR (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:317:y:2024:i:2:p:249-272

DOI: 10.1016/j.ejor.2023.09.026

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European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

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